In either an industrial or commercial setting, a malfunctioning machine such as an imaging machine can impair a business severely. Thus, it is essential that a malfunctioning imaging machine be repaired quickly and accurately. Usually, during a malfunction of an imaging machine such as a computed tomography (CT) or a magnetic resonance imaging (MRI) machine, a field engineer is called in to diagnose and repair the machine. Typically, the field engineer will run a system performance test to analyze the image quality or the state of the imaging machine. The system performance test generates waveform data which provides a "signature" of the operation of the imaging machine. The waveform data comprises data sets of various readouts and slice combinations. After the system performance test has been run, the field engineer sends the data sets to a service engineer at a remote location for help in diagnosing the malfunction. The service engineer analyzes the data sets and uses their accumulated experience at solving imaging machine malfunctions to find any symptoms that may point to the fault. The field engineer then tries to correct the problem that may be causing the machine malfunction based on the diagnosis provided by the service engineer. If the data sets contain only a small amount of information, then this process will work fairly well. However, if the data sets contains a large mount of imprecise information, as is usually the case for large complex devices, then it is very difficult for the field engineer and the service engineer to quickly diagnose a fault. Therefore, there is a need for a system and method that can quickly diagnose a malfunctioning imaging machine from waveform data sets containing large amount of imprecise information.